This invention relates to the detection of signals in interference and more particularly to an improved spatial acquisition system utilizing signal subspace tracking.
The purpose of a spatial acquisition process is to detect signals in a cluttered RF environment. The ability to ascertain when a new signal birth occurs is extremely important in modern warfare to quickly identify the signal type and the location of its transmitter. Finding signals and identifying them is particularly troublesome when listening in on a wide range of frequencies, so called wideband signal acquisition.
In addition to the problem of discerning a signal from background environment noise, one is faced with the problem of detecting signals occupying the same frequency band, so called co-channel interferers. Single antenna (non-spatial) detection systems attempt to detect new signal energy through changes in amplitude in narrowband frequency channels (i.e. a channelizing receiver). Attempts to detect co-channel signals require that the new signal power exceeds the existing signal level by a significant amount (Signal on Signal detection) in order to trigger a new signal detection. Obviously, this requirement for increased signal power only works in a limited number of situations, and often the signal of interest will be received in a channel at lower power than an existing interferer, making it difficult if not impossible to detect.
It will be shown that by using an array of antennas and processing in a spatial manner, one is able to discern the existence of multiple signals using detection via rank mechanisms and angle of arrival. This adds an additional dimension other than amplitude and allows one to separate the signals in a manner that one cannot do by using a single antenna. As an example, many military radios happen to also occupy the same frequency band as TV stations. If one uses an aircraft for surveillance and one uses a single channel acquisition system, ie one with a single antenna, one is going to detect or see the TV station because it is the stronger of the two signals. In a battlefield scenario the military signal might be hidden amongst the frequency content of the TV station. On the ground military personnel can talk back and forth because they do not receive the signals from the TV station, but in the air one might detect signals from both of these sources. It is therefore important to be able to spatially separate the military signals from the TV signals so as to identify where the signals came from.
A prior approach devised by Brian G. Agee of AGI Engineering Consulting, provided beamforming on new signal births or signals which have burst characteristics, off/on. The beamformer xe2x80x98weightsxe2x80x99 are calculated by looking at the so-called xe2x80x98change matrixxe2x80x99 or the new covariance matrix versus the old covariance matrix and generates the beamformer weights which maximize the Signal to Interference ratio for the xe2x80x98newxe2x80x99 signal. Therefore, a wide bandwidth detection system based on this approach, and assuming frequency channelization, would require application of this technique on all frequency channels all the time and looking at the output of the beam former to determine whether new energy had arrived at the array. If the amount of output power from the beamformer exceeded a threshold, Agee""s process would declare that occurrence a new detection.
A weakness of the Agee process is that the focus is strictly on new energy detection. It considered the old signal environment to be background interference, and simply looked for changes to the background. The Agee process only allowed for a change of one signal or a rank change of one to this background. This prevents for instance deciding that there are two or more new incoming signals. Unfortunately with that approach there was never an understanding of what the background environment was. For instance, signals that were always up would be ignored because they were considered part of the background environment. In other words the Agee process was optimal for new energy alarms or for bursty signals, ie. signals that go off to on and vice versa. However, the Agee process had little benefit for tracking existing signals and was limited to leading edge clustering, only. As a result, to build a more robust reconnaissance system, one needs to not only be interested in signals that are bursty or new but also to be cognizant of the signals that are already in the environment. In order to know the number of signals present, both old and new, one required a separate processing path to look for the signals that were stable, e.g. the ones that were not bursty in nature.
In short, assuming that multiple signals arrived at an antenna array, in the past there was no particularly good way of ascertaining how many steady state signals were there as opposed to bursty signals. What this means is that if for instance there were four signals that were constantly on and two more signals arrived, the Agee process would associate the four signals with the background environment, i.e., noise, and associate the two new signals as a combined signal in the signal subspace.
Therefore, one of the key requirements for the detection of a signal in a dense RF environment is to determnine the total number of signals that are impinging on the array. Existing block processing techniques focus on eigen-space decomposition of the data covariance matrix, then, applying rank estimation techniques such as Minimum Data Length, MDL or Akaike Information Criteria, AIC to separate the signal and noise subspace components. The rank of the signal subspace is equivalent to the number of spatially separated signals impinging on the array. Standard algorithms for performing eigen decomposition include the symmetric QR algorithm and Jacobi rotations. A limiting assumption of these signal subspace decomposition techniques is that the total number of signals impinging on the array is less than the number of array elements or degrees of freedom, DOF. These block processing approaches to signal subspace decomposition have been developed for tasked DF systems, that is to capture a buffer full of data and then post-process the buffered data to determine how many signals are in the buffer. Thereafter one can employ a superresolution DF algorithm, such as the MUSIC algorithm, to determine angle of arrival information.
Articles relating to Superresolution DF and subspace tracking are as follows: Multiple Emitter Location and Signal Parameter Estimator by Ralph O. Schmidt, IEEE Proceedings of the RADC Spectrum Estimation Workshop, October 1979; Adaptive Rank Estimator for Spherical Subspace Trackers by Aleksander Kaveie and Bing Yang, IEEE Transactions on Signal Processing, Vol. 44, No. 6, June 1996; An Extension of the PASTd Algorithm to Both Rank and Subspace Tracking, Bin Yang, IEEE Signal Processing Letters, Vol. 2, No. 9, September 1995; and, Two Algorithms For Fast Approximate Subspace Tracking by Edward C. Read, Donald W. Tufts and James W. Cooley, IEEE Transactions on Signal Processing, Vol. 47, No. 7, July 1999.
For wide bandwidth signal detection across a multiple antenna array, spatial acquisition requires N antennas [e.g., N=8 antennas]. Theoretically, the largest rank or number of signals that can be found with 8 antennas is 7 signals, the other one being the noise signal. With 8 antennas there are eight degrees of freedom. In a wideband system all degrees of freedom can easily be occupied. This is because in a wideband application for instance having a bandwidth of 80 MHz the likelihood that there will be more than 7 signals is virtually guaranteed. What one needs to do is to break up the large band into smaller channelized frequency bands such that the likely number of signals in each of those smaller frequency bands is much less than the full eight, preferably four or less for most practical arrays. The narrower a bin, the smaller the likely number of potential interferers that will exist in the frequency bin.
However, with very narrow frequency bins, it is likely that signals can straddle many different bins. One therefore needs to do signal subspace determination on each one of those bins, independently, and make decisions at a later date using a technique called xe2x80x9cclusteringxe2x80x9d to determine whether signals from adjacent frequency bins should be grouped as a single signal.
In short, when surveilling a wide frequency range such as between 20 MHz and 100 MHz, assuming an 8 element array, for the 80 MHz band the likelihood of there being 4-8 interfering signals is very high. In order to assure there being no more than 4 signals in a band, the original 80 MHz band is sub-channeled into 25 KHz bins. The subdivision however reinforces the need for some ability to be able to separate out overlapping signals or signals which overlap multiple bins and overlap each other.
This, in addition to frequency selectivity, drives the wide band acquisition system towards frequency channelization.
If one extends these block processing techniques to the streaming wideband spatial acquisition problem, one is faced with multiple channelized frequency bins each requiring independent processing. These block eigenanalysis techniques are typically very time consuming and would be prohibitive for the wideband spatial acquisition problem. One needs to employ more efficient update approaches to meet the throughput requirements of multiple bins of streaming data. More efficient subspace tracking techniques such as those developed by Bin Yang and Edward Real assume streaming data across multiple antennas and attempt to determine the signal subspaces recursively, by updating previous signal subspace estimates. This reduces computational complexity and is a much more efficient process than block processing. The efficiencies afforded by Yang and Reals for a single frequency channel permit wideband signal acquisition by applying these techniques to multiple independent frequency bins.
As previously discussed, wideband signal acquisition requires the break up of the wide band into smaller channelized frequency bands or bins and by so doing limiting the number of signals in any frequency bin. This is done by making the frequency bins narrow enough so that it is unlikely that the number of signals will occupying a given frequency bin exceeds the number of antennas. However, channelizing the entire wide bandwidth into subchannels compounds the problem, because one now has to do the spatial or subspace tracking over large numbers of frequency bins. One therefore needs to focus on an efficient implementation of a subspace tracker because one needs to do it across many frequency-channelized bins. Bin Yang and Edward Real, while achieving great efficiencies in subspace tracking in a single frequency channel, did not identify how to extend to multi-bin wideband applications.
In order to provide a system for the detection and tracking of narrowband signals arriving at an antenna array operating over a large bandwidth in a crowded RF environment, in the subject invention the large bandwidth is subdivided into subchannels or narrow frequency bins and real time subchannel spatial tracking is done across all bins to arrive at the number of signals and associated eigen vectors in these bins. The result is that the number of signals from the subspace tracking is available to a new signal detector to detect a change in the number of signals present, whether or not the signals have persisted over a long period of time. This detector provides a bin-up alarm to a clustering algorithm operating on all bins. The clustering is done not only on a time/frequency basis but also using angle of arrival from a direction finding algorithm, because, the output from subspace tracking, ie number of signals and associated signal subspace eigenvectors, can be used by a multiple signal AOA algorithm and beamformer.
Cluster analysis ascertains with some certainty that signals which overlap adjacent bins are in fact the same signal. Having ascertained what bins have a specific signal, the energy from only those bins is read out through a multi-bin combiner. The recombined signal, after beamforming, is passed as a xe2x80x9csignalxe2x80x9d-up alarm to a signal analysis unit for analyzing the multi-bin combined xe2x80x9csignalxe2x80x9d.
Since very few analyzers are usually provided, it is important that only validated signals be analyzed. Validation is accomplished through subspace tracking on multiple subchannels, coupled with AOA processing using the output from the subspace tracker, time/frequency clustering techniques and beamforming. With this type of validation either fewer signal analyzers need to be provided or those provided can be used more efficiently.
What is therefore provided is an efficient subspace tracker applied to the spatial acquisition problem. This means that the number of signals is determined for all bins vs. time. Knowledge of rank vs. time on a bin-by-bin basis provides a new detection criteria to determine when the number of new signals exceeds the number of old signals. Note that the number of new signals can be greater than one, unlike what can be detected by the Agee process.
It is an important feature of the subject system that the elements of the system can be partitioned so that new subspace tracking algorithms can be substituted for old ones without affecting any of the other processes.
Moreover, if one wants even wider bandwidths, one can simply add more subchannels or frequency bins, making the system scalable.
The problem of processing a large number of sub-channels is solved by utilizing efficient subspace trackers and multi-bin clustering, which includes the use of direction finding information. The signal subspace tracker provides the required input to the direction finding algorithm, and thus solves the problem in the past of gaining angle of arrival data from another subsystem.
Allowing for instantaneous rank changes of greater than one supports the detection of multipath in which the same original signal arrives from multiple directions.
In summary, a system is provided for assisting in the detection and tracking of narrowband signals arriving at an antenna array operating over a wide detection bandwidth and in a crowded RF environment. Since the nature of the detection mission is constrained to be a general search, the system does not attempt to detect signals of interest via matched filtering mechanisms (i.e. training sets), but exploits general properties such as power, frequency, time and angle of arrival. For the purposes of providing sufficient Frequency/Time resolution as well as to avoid array overloading in the detection process, the digitized wideband streaming data is frequency channelized using a sufficiently high revisit rate for the signal set of interest, constrained by the required feature detection accuracy or environment adaption rates. Within each frequency subchannel, efficient array signal subspace tracking techniques are used to separate and track spatially separated cochannel signals. Subspace tracking allows the efficient update of the signal subspace, useful for direction finding and copy applications, as well as determining the number of signals present in a frequency channel. Since the frequency channelization of the detection system may not match that of the detected signal, the combination of Time/Frequency/Space information is used to cluster or group frequency subchannels and provide a higher degree of signal detection capability with an increased robustness against false signal detections.